Using Progress-Monitoring Data to Improve Instructional Decision Making

Author(s):  
Pamela M. Stecker ◽  
Erica S. Lembke ◽  
Anne Foegen
2021 ◽  
pp. 153450842110350
Author(s):  
Jillian Dawes ◽  
Benjamin Solomon ◽  
Daniel F. McCleary ◽  
Cutler Ruby ◽  
Brian C. Poncy

The current availability of research examining the precision of single-skill mathematics (SSM) curriculum-based measurements (CBMs) for progress monitoring is limited. Given the observed variance in administration conditions across current practice and research use, we examined potential differences between student responding and precision of slope when SSM-CBMs were administered individually and in group (classroom) conditions. No differences in student performance or measure precision were observed between conditions, indicating flexibility in the practical and research use of SSM-CBMs across administration conditions. In addition, findings contributed to the literature examining the stability of SSM-CBMs slopes of progress when used for instructional decision-making. Implications for the administration and interpretation of SSM-CBMs in practice are discussed.


2021 ◽  
Vol 54 (4) ◽  
pp. 239-242
Author(s):  
Christine A. Espin ◽  
Natalie Förster ◽  
Suzanne E. Mol

This article serves as an introduction to the special series, Data-Based Instruction and Decision-Making: An International Perspective. In this series, we bring together international researchers from both special and general education to address teachers’ use (or non-use) of data for instructional decision making. Via this special series, we aim to increase understanding of the challenges involved in teachers’ data-based instructional decision making for students with or at-risk for learning disabilities, and to further the development of approaches for improving teachers’ ability to plan, adjust, and adapt instruction in response to data.


2019 ◽  
Vol 6 (2) ◽  
Author(s):  
Alyssa Friend Wise ◽  
Yeonji Jung

The process of using analytic data to inform instructional decision-making is acknowledged to be complex; however, details of how it occurs in authentic teaching contexts have not been fully unpacked. This study investigated five university instructors’ use of a learning analytics dashboard to inform their teaching. The existing literature was synthesized to create a template for inquiry that guided interviews, and inductive qualitative analysis was used to identify salient emergent themes in how instructors 1) asked questions, 2) interpreted data, 3) took action, and 4) checked impact. Findings showed that instructors did not always come to analytics use with specific questions, but rather with general areas of curiosity. Questions additionally emerged and were refined through interaction with the analytics. Data interpretation involved two distinct activities, often along with affective reactions to data: reading data toidentify noteworthy patterns and explaining their importance in the course using contextual knowledge. Pedagogical responses to the analytics included whole-class scaffolding, targeted scaffolding, and revising course design, as well two new non-action responses: adopting a wait-and-see posture and engaging in deep reflection on pedagogy. Findings were synthesized into a model of instructor analytics use that offers useful categories of activities for future study and support


2014 ◽  
Vol 107 (8) ◽  
pp. 639

Assessment is an integrated part of mathematics instruction that guides and enhances teaching and learning. A key aspect of instructional decision making is the alignment of standards, curriculum, instruction, and assessment. The MT Editorial Panel is interested in manuscripts that address one or more of the following themes related to assessment.


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